Memetic algorithms (MAs) are widely recognized to have better convergence capability than their conventional counterparts. Due to its good robustness and universality, differential evolution (DE) has been frequently used as the global search method in MAs. However, on account of the limited performance of the conventional local search operators, the performance of previous DE-related MAs still needs further improvement. In this paper, we implement more efficient evolutionary algorithms (EAs) as the local search techniques in an adaptive MA framework to form two MA(DE-LS) variants, and investigate their impacts. In order to comprehensively show the effectiveness and efficiency of MA(DE-LS), we experimentally compare it with state-of-the-art EAs, DE-based MAs and other MAs.

During the last decade, a large number of evolutionary algorithmic variants have been proposed for diverse optimization tasks, most of which are practical engineering applications. In the previous research, one variant is always designed for one specific engineering application. In IEEE Congress on Evolutionary Competition, the numerical optimization competition is held to benchmark different optimization algorithms for more general applications. In this paper, we conduct the optimization method estimation of distribution and differential evolution (ED-DE) by implementing a two-stage ensemble idea, whose effectiveness and efficiency has been experimentally verified.

Large-scale global optimization (LSGO) is a very important and challenging task in optimization domain, which is embedded in many scientific and engineering applications. In this paper, a two-stage based ensemble optimization evolutionary algorithm (EOEA) is designed to handle LSGO problems. The performance of EOEA is evaluated on the test functions provided by the LSGO competition of IEEE Congress of Evolutionary Computation (CEC 2010). Compared with some previous LSGO algorithms, EOEA demonstrates better performance.

Economic Load Dispatch (ELD) optimization is an important and difficult task in power system planning. Previously, most of the research mainly focused on proposing various evolutionary algorithms (EAs) to pursue better results of ELD problems. However, few comprehensive analysis of the effects of various constraint handling techniques (CHTs) on the performance of EA-based techniques are available so far. In this paper, we try to fill this gap by experimentally testing the algorithmic variants of combining four effective and widely used EAs with three CHTs. From the experimental results on the ELD problems with valve-point and those problems with both valve-point and multiple-fuel effect, several important conclusions can be achieved, including 1) for the low scale ELD problem with valve-point only, the selection of EAs is more important than CHTs; 2) for the large scale problems, CHTs play crucial roles; 3) the appropriate combination of EA and CHT is helpful to achieve better performance. This study is also expected to provide solid basis for further strengthening the robustness of EAs for ELD optimization. It is also interesting to observe that the experimental results obtained in this paper are much better than those of the previous effective ELD optimization algorithms.

As the research of dynamic optimization arising, memory-based strategy has gained public attention recently. However, few studies on developing dynamic multi-objective optimization algorithms and even fewer studies on multi-objective memory-based strategy were reported previously. In this paper, we try to address such an issue by proposing several memory-based multi-objective evolutionary algorithms and experimentally investigating different multi-objective dynamic optimization schemes, which include restart, explicit memory, local search memory and hybrid memory schemes. This study is to provide pre-trial research of how to appropriately organize and effectively reuse the changed Pareto-optimal decision values (i.e., Pareto-optimal solutions: POS) information.

Large scale global optimization (LSGO) is a very important and extremely difficult task in optimization domain, which is urgently needed for scientific and engineering applications. Recently, decompose-and-conquer strategy has become a promising method to handle LSGO problems. In this paper, we propose a new strategy variance priority (VP) to improve the classical cooperative co-evolution framework. Based on this proposed strategy, a new LSGO algorithm, variance priority based cooperative co-evolution differential evolution (VP-DECC), is developed. The advantages of VP strategy over the other decompose-and-conquer strategies are experimentally investigated. Especially, it has shown excellent performance in dealing with more complex problems.

Classical NK-landcape model was designed for analyzing optimization and evolution process in binary solution space, so it can not be used to analyze real coded optimization algorithms (RCOAs) directly, which work in continuous solution space directly. In this paper, the concept of NK-landscape model is extended to the continuous space, and a new NK-landscape model with continuous space is proposed. The new model is powerful and comprehensive with simple structure and flexible formula. Therefore, it can be used to construct test functions of various types of linkages for analyzing various performances of RCOAs. The feasibility of the proposed model is testified via experiments with 3 well-known RCOAs, ( i.e. covariance matrix adapting evolutionary strategy (CMA-ES), differential evolution (DE), neighborhood search differential evolution (NSDE)). The results show that the new model can reveal the merits and demerits of RCOAs effectively.

A univariate EDA denoted as ldquoLSEDA-glrdquo for large scale global optimization (LSGO) problems is proposed in this paper. Three efficient strategies: sampling under mixed Gaussian and Levy probability distribution, standard deviation control strategy and restart strategy are adopted to improve the performance of classical univariate EDA on LSGO problems. The motivation of such work is to extend EDAs to LSGO domain reasonably. Comparison among LSEDA-gl, EDA with standard deviation control strategy only (EDA-STDC) and similar EDA version ldquocontinuous univariate marginal distribution algorithmrdquo UMDAc is carried out on classical test functions. Based on the general comparison standard, the strengths and weaknesses of the algorithms are discussed. Besides, LSEDA-gl is tested on 7 functions with 100, 500, 1000 dimensions provided in the CECpsila2008 Special Session on LSGO. This work is also expected to provide a comparison result for the CECpsila2008 special session.